Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics

Akhmet, Marat
Tleubergenova, Madina
Zhamanshin, Akylbek
In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients to final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided.
Chaos, Solitons and Fractals
Citation Formats
M. Akhmet, M. Tleubergenova, and A. Zhamanshin, “Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics,” Chaos, Solitons and Fractals, vol. 178, pp. 0–0, 2024, Accessed: 00, 2024. [Online]. Available: